[H]ard|OCP were at AMD's launch of the new EPYC family of server CPUs and captured the presentation and slide deck in a series of photos you can take a look at right here. They cover the work being done with HP and Dell, as well as with internet service providers such as Microsoft's Azure platform and China's Baidu. They even give you a look at some of the products which will be launched running on Supermicro platforms. AMD is looking very attractive to server builders at the moment, a feeling you may already have garnered from reading Ryan's take on EPYC.

"AMD held it official EPYC enterprise CPU launch today in Austin, TX. If you are not aware of EPYC, it is quite simply AMD's effort to get back into the datacenters that are now firmly held by Intel Xeon processors. What do you get when you take 4 Ryzen 7 CPUs and put those down on a single package with Infinity Fabric? You would be correct, its EPYC."

Ryan was not the only one at AMD's Radeon Instinct briefing, covering their shot across NVIDIA's HPC products. The Tech Report just released their coverage of the event and the tidbits which AMD provided about the MI25, MI8 and MI6; no relation to a certain British governmental department. They focus a bit more on the technologies incorporated into GEMM and point out that AMD's top is not matched by an NVIDIA product, the GP100 GPU does not come as an add-in card. Pop by to see what else they had to say.

"Thus far, Nvidia has enjoyed a dominant position in the burgeoning world of machine learning with its Tesla accelerators and CUDA-powered software platforms. AMD thinks it can fight back with its open-source ROCm HPC platform, the MIOpen software libraries, and Radeon Instinct accelerators. We examine how these new pieces of AMD's machine-learning puzzle fit together."

AMD Enters Machine Learning Game with Radeon Instinct Products

NVIDIA has been diving in to the world of machine learning for quite a while, positioning themselves and their GPUs at the forefront on artificial intelligence and neural net development. Though the strategies are still filling out, I have seen products like the DIGITS DevBox place a stake in the ground of neural net training and platforms like Drive PX to perform inference tasks on those neural nets in self-driving cars. Until today AMD has remained mostly quiet on its plans to enter and address this growing and complex market, instead depending on the compute prowess of its latest Polaris and Fiji GPUs to make a general statement on their own.

The new Radeon Instinct brand of accelerators based on current and upcoming GPU architectures will combine with an open-source approach to software and present researchers and implementers with another option for machine learning tasks.

The statistics and requirements that come along with the machine learning evolution in the compute space are mind boggling. More than 2.5 quintillion bytes of data are generated daily and stored on phones, PCs and servers, both on-site and through a cloud infrastructure. That includes 500 million tweets, 4 million hours of YouTube video, 6 billion google searches and 205 billion emails.

Machine intelligence is going to allow software developers to address some of the most important areas of computing for the next decade. Automated cars depend on deep learning to train, medical fields can utilize this compute capability to more accurately and expeditiously diagnose and find cures to cancer, security systems can use neural nets to locate potential and current risk areas before they affect consumers; there are more uses for this kind of network and capability than we can imagine.